#=====================全局参数==================================
cuda = True                 #是否有cuda
data_type = 'vehicle'       #数据集类型，['vehicle','mpii']
#=======================数据集的设置==============================
vehicle_is_test = False

vehicle_ckpt_save_dir = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/关键点/Vehicles'
vehicle_train_txt_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/车辆关键点/数据集/train.txt'    #训练或者测试的txt路径
vehicle_valid_txt_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/车辆关键点/数据集/valid.txt'
vehicle_ckpt_resume_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/关键点/Vehicles/last_ckpt.pth'   #如果有这个权重就可以使用
vehicle_output_dir = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/车辆关键点/数据集/test_results'         #生成可视化数据的目录

mpii_is_train = True
mpii_flip = False
mpii_scale_factor = 0.25
mpii_rot_factor = 30
mpii_sigma = 2
mpii_select_data = False
mpii_target_type = 'gaussian'
mpii_ckpt_save_dir = "/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/关键点/Mpii"      #权重保存路径
mpii_images_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/mpii_human_pose_v1/images'
mpii_json_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/mpii_human_pose_v1/annot/valid.json'
mpii_ckpt_resume_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/关键点/Mpii/last_ckpt.pth'
mpii_output_dir = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/关键点/Mpii/新建文件夹'
#===============================模型的设置=======================
model_style = 'pytorch'     #['caffe','pytorch']
num_layers = 50             #[50,18,24,101,152]
num_keypoints = 16         #关键点数量
use_target_weight = False   #是否计算隐藏点与否的loss信息
#========================日志===================================
test_output_dir = vehicle_output_dir if data_type == 'vehicle' else mpii_output_dir
pre_train = ""              #backbone的权重
#=========================优化器，学习率===========================
optimizer_method = "adam"   #[sgd,adam],adam比sgd效果好
lr_method = 'step'          #[step,adaptive,multiStep]
train_lr_step = [90, 110]   #多step学习率的间隔
init_lr = 0.001             #初始学习率
#=======================训练=====================================
start_epoch = 0             #开始epoch
total_epoch = 500           #总共的epoch
train_batch_size = 64       #训练的batch size，根据硬件条件修改
val_batch_size = 128        #验证的batchsize，根据硬件条件修改
continue_train = True       #是否继续训练，第一次训练设置为False
img_size = 256              #输入模型的图片尺寸
heatmap_size = 64           #heatmap尺寸
ckpt_save_dir = mpii_ckpt_save_dir if data_type == 'mpii' else vehicle_ckpt_save_dir
ckpt_resume_path = mpii_ckpt_resume_path if data_type == 'mpii' else vehicle_ckpt_resume_path
#==================================测试集的参数========================
frequent:int = 0            #记录日志的频率
test_model_file = mpii_ckpt_resume_path if data_type == 'mpii' else vehicle_ckpt_resume_path       #模型的文件路径
use_detect_bbox = ''        #使用detect bbox
flip_test = False            #翻转数据集
post_process = True         #后处理
shift_heatmap = True        #转换热力图
coco_bbox_file = ''         #coco数据集的检测bbox文件
test_batch_size = 1
print_freq = 10            #每隔多少iter打印测试信息
test_vehicle_txt_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/车辆关键点/数据集/test.txt'

test_mpii_json_path = ''
test_mpii_mat = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/mpii_human_pose_v1/annot/gt_valid.mat'
#======================进行验证的时候，是否输出对应的图片==============
save_batch_images_gt = False
save_batch_images_pred = True
save_heatmaps_gt = False
save_heatmaps_pred = False

#================demo.py的参数===================================
demo_mode_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/关键点/Vehicles/last_ckpt.pth'
demo_images_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/车辆关键点/测试图片'
demo_images_result_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/公司数据/车辆关键点/测试图片结果'

